Matplotlib swordsman - a stylist who can draw without tools and code

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Trilogy on the eve of painting

canvas

• adopt plt.figure To create a canvas ——plt.figure(figsize(a,b), facecolor=‘r’)

Change Chinese font

• Matplotlib Encountered the problem of Chinese display box in
  • adopt plt.rcParams[‘font.sans-serif’] = ‘Microsoft YaHei’ solve
  • The Chinese fonts that can be set are SimSun（ Song style ）,SimHei（ In black ）,Kaiti（ Regular script ） etc.

Negative signs show problems

• adopt plt.rcParams[‘axes.unicode_minus’] = False solve

Stylist —— Style parameters

Most drawing methods have style options , Can access the current call drawing method , Or from Artist Upper “setter” call . In the inside picture , We set it manually Color 、 Lineweight and linetype plot, We set the linetype of the second line to call set_linestyle.

import numpy as np
import matplotlib.pyplot as plt

#  Load Fonts 
plt.rcParams['font.sans-serif'] = ['SimHei']  #  Specify default font 
#  Show minus sign 
plt.rcParams['axes.unicode_minus'] = False

data1, data2, data3, data4 = np.random.randn(4, 150)  #  establish 4 A random data set 

fig, ax = plt.subplots(figsize=(10, 6))
x = np.arange(len(data1))
ax.plot(x, np.cumsum(data1), color='g', linewidth=4, linestyle='--')
l, = ax.plot(x, np.cumsum(data2), color='b', linewidth=3)	#  This is called set_linestyle
l.set_linestyle(':')
plt.show()

Output graph ：

Color

Matplotlib There is a very flexible color array , Will use a variety of colors . That is, for a scatter mapping , The edge and interior of the mark can be different colors ：

• color—— Assign colors
  
• alpha（0-1）—— transparency
• colormap—— Color board

data1, data2, data3, data4 = np.random.randn(4, 150)  #  establish 4 A random data set 
x = np.arange(len(data1))

fig, ax = plt.subplots(figsize=(10, 6))
ax.scatter(data1, data2, s=50, facecolor='b', edgecolor='r')	#  The edge color of the mark is red , The interior color is blue 
plt.show()

Output graph ：

linear

• linewidth—— Line width , It can be abbreviated as ：’lw‘, Set the thickness of the line
• linestyle—— Linetype
  

Mark

• marker—— Mark

• markerwidth—— Tag size

• Unfilled tags
  

• Fill mark
  

fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(data1, 'o', label='data1')
ax.plot(data2, 'd', label='data2')
ax.plot(data3, 'v', label='data3')
ax.plot(data4, 's', label='data4')
ax.legend()
plt.show()

Output graph ：

• style Parameters , Can contain linestyle,marker,color

import pandas as pd
import numpy as np

# style Parameters , Can contain linestyle,marker,color

ts = pd.Series(np.random.randn(1000).cumsum(), index=pd.date_range('1/1/2000', periods=1000))
ts.plot(style='--g.')
# style →  Style string , This includes linestyle（-）,marker（.）,color（g）
plt.show()

Output graph ：

Mark parcels —— The basic elements

Axis labels and text

• set_xlabel, set_ylabel, and set_title Used to add text at the specified position
• Text can also be used directly text Add to drawing

Add chart title

• adopt plt.title Can be in Matplotlib Set title
  • Font styles can be created by fontdict Set it up
  • fontsize—— Set the title size
  • adopt loc Parameter can set the title display position , The supported parameters are ：
    • center（ In the middle ）
    • left（ Keep to the left ）
    • right（ Keep right ）

Add axis title

• Add axis title
  • adopt plt.xlabel and plt.ylabel You can add... Separately x Axis and y The title of the axis
  • It can also be done by fontdict Configure font styles
  • fontsize—— Set the title size
  • labelpad You can set the distance between the coordinate axis and the title

Add data label ——text

Can pass plt.text Add text to the chart , But only one point can be added at a time , So if you want to add labels to every data item , We need to pass for loop To carry out

• plt.text There are three important parameters ：
  • x、y 、s, adopt x and y Determine the display position ,s For the text that needs to be displayed
  • And then there is va and ha Two parameters set the display position of the text （ Keep to the left 、 Keep right 、 Center, etc ）
  • Spaces require —— Transfer characters \
  • fontdict—— Set the size and color of text words , Need to use dict Form into

mu, sigma = 120, 20
x = mu + sigma * np.random.randn(1000)
fig, ax = plt.subplots(figsize=(10, 6))
#  Histogram of data 
n, bins, patches = ax.hist(x, 50, density=1, facecolor='C0', alpha=0.75)

ax.set_xlabel('Length [cm]')    # x Axis labels 
ax.set_ylabel('Probability')    # y Axis labels 
ax.set_title('Aardvark lengths\n (not really)')     #  title 
#  ad locum  r  Before the title string, it means that the string is   original   character string , Instead of treating backslashes as  python  escape 
ax.text(75, .025, r'$\mu=120,\ \sigma=20$')     #  To add text —— Here is the use of mathematical expressions in text 
ax.axis([55, 175, 0, 0.03])
ax.grid(True)   #  Adding grid 
plt.show()

Output graph ：

notes —— To add text annotate

You can also mark points on the figure , Usually by connecting arrows xytext Point to xy

• annotate() Basic use

  • text—— It's the text of the comment
  • xy—— It's the coordinates of the points that need to be annotated , Used to locate the position to be marked
  • xytext—— Is the coordinates of the annotation text （ Mark the location ）, Prompt the position of the text to be displayed
  • arrowprops—— It is the setting of arrow type and arrow radian in the figure , Need to use dict Form into
  • xycoords=‘data’—— Choose a location based on the value of the data , Set up xy How to locate , such as data Indicates that the coordinate axis scale value is used for positioning
  • textcoords=‘offset points’——xy Deviation value , Same as xycoords, Set up xytext How to locate

t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2 * np.pi * t)

fig, ax = plt.subplots(figsize=(10, 6))
line, = ax.plot(t, s, linewidth=2)
ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
            arrowprops=dict(facecolor='black', shrink=0.05))

ax.set_ylim(-2, 2)
plt.show()

Output graph ：

Legend has it that —— legend legend

adopt plt.legend You can add a legend to the chart , The legend is usually used to explain the data of each series in the chart

• legend—— Show Legend
• fontsize—— Set font size
• By setting handles Parameter to select the content displayed in the legend
• loc—— Indicate location
  • ‘best’ : 0 ( Adaptive way )
  • ‘upper right’ : 1
  • ‘upper left’ : 2
  • ‘lower left’ : 3
  • ‘lower right’ : 4
  • ‘right’ : 5
  • ‘center left’ : 6
  • ‘center right’ : 7
  • ‘lower center’ : 8
  • ‘upper center’ : 9
  • ‘center’ : 10

data1, data2, data3 = np.random.randn(3, 150)  #  establish 3 A random data set 
x = np.arange(len(data1))

fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(np.arange(len(data1)), data1, label='data1')
ax.plot(np.arange(len(data2)), data2, label='data2')
ax.plot(np.arange(len(data3)), data3, 'd', label='data3')
ax.legend(loc='best')     #  Add legend 
plt.show()

Output graph ：

Add grid lines ——grid

• By adding grid lines, users can easily see the approximate value of data items , When displaying all data labels through the above , It may make the whole chart more messy , We can choose to use gridlines to show the approximate data values of data items

• Parameters b by True Show gridlines when

• axis Support x、y、both Three values , respectively

  • x—— Vertical gridlines
  • y—— Horizontal gridlines
  • both—— Vertical and horizontal

• Other linetype configurations are similar to the parameters of configuring polyline style , Such as ： Linetype 、 Line width 、 Color

Leave the canvas blank ——tight_layout()

Because the chart and Figure Insufficient white space , The legend cannot be displayed completely .Matplotlib Inside the module tight_layout() function , Settings are available pad Parameters are shown in the chart and Figure Set a blank space between （ There is no need to set pad Parameters ）

• plt.tight_layout()—— Tighten the space around , Expand the usable area of the drawing area
• h_pad /v_pad You can set the height separately / Width blank

Shaft scale and scale

• tick params（）—— Set axis ：
  • Scale size （labelsize）
  • （ Scale line ） Color （color）
  • Range of application （axis）
• tick_params（axis=‘xx’,labelsize=b,color=‘r’）
  • labelsize Of b Represents the scale size
  • If axis Of xx yes both Represents the application to x and y Axis , If xx yes x Represents the application to x Axis , If xx yes y Represents the application to y Axis .
  • color Is to set the line color of the scale , for example ,red For red

x = np.linspace(-3, 3, 50)
y1 = 2 * x + 1
y2 = x ** 2

plt.figure(num=3, figsize=(8, 5))
plt.plot(x, y2)
plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--')
plt.tick_params(axis='both', labelsize=18, color='red')
plt.show()

Output graph ：

Coordinate range and axis scale

• occasionally , Our axis scale may not be a series of numbers , But some words , Or we want to adjust the sparsity of the scale of the coordinate axis

• At this time , You need to use plt.xticks() perhaps plt.yticks() To adjust ： First , Use np.linspace Define the new scale range and number ： The scope is (-1,2); The number is 5.

  • plt.axis—— Simultaneous setting x,y Axis coordinate range
  • plt.xlim—— Set up x Axis range ：(-1, 2)
  • plt.ylim—— Set up y Axis range ：(-2, 3)
  • plt.xticks—— Set up x Axis scale and name ： The scope is (-1,2); The number is 5.
  • plt.yticks—— Set up y Axis scale and name ： Scale is [-2, -1.8, -1, 1.22, 3]; The name of the corresponding scale is [‘really bad’,’bad’,’normal’,’good’, ‘really good’].

x = np.linspace(-3, 3, 50)
y1 = 2 * x + 1
y2 = x ** 2

plt.figure(num=3, figsize=(8, 5))
plt.plot(x, y2)
plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--')

plt.axis([-1, 2, -2, 3])	#  Use  axis（） Set up x,y The minimum and maximum scales of the shaft 
plt.xlim((-1, 2))  # x Axis coordinate range 
plt.ylim((-2, 3))  # y Axis coordinate range 

new_ticks = np.linspace(-1, 2, 5)
plt.xticks(new_ticks)  # x Axis scale ( You can also set the name here, such as bar chart )
plt.yticks([-2, -1.8, -1, 1.22, 3], [r'$really\ bad$', r'$bad$', r'$normal$', r'$good$', r'$really\ good$'])  # y Shaft scale and name 
plt.show()

Output graph ：

Adjust the scale and border position

• Use .xaxis.set_ticks_position Set up x The position of a coordinate scale number or name ：
  • bottom.（ All positions ：top,bottom,both,default,none）
• Use .spines Set borders ：x Axis ; Use .set_position Set border position ：y=0 The location of ;（ Position all attributes ：outward,axes,data）

The code is as follows ：

ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data', 0))

figure ：

• Use .yaxis.set_ticks_position Set up y The position of a coordinate scale number or name ：
  • left.（ All positions ：left,right,both,default,none）
• Use .spines Set borders ：y Axis ; Use .set_position Set border position ：x=0 The location of ;（ Position all attributes ：outward,axes,data）

The code is as follows ：

ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))

figure ：

Set the image border color

Careful partners may notice , Our image coordinate axis is always composed of four lines up, down, left, right , We can also modify them ：

• plt.gca()—— Get the current axis information
• Use .spines—— Set borders ： Right border
• Use .set_color—— Set border color ： Default white
• Use .spines Set borders ： On the border
• Use .set_color Set border color ： Default white

The code is as follows ：

ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')

In the last

After reading the knowledge points , It's your turn to pass “ Stylist ” I'm addicted to it . Interested partners can combine the above learning , Do it yourself , Only in this way can we firmly remember ！

One ：
Draw a straight line y = x-1, The line type is dashed , The line width is 1, The ordinate range （-2,1）, The abscissa range （-1,2）, The horizontal and vertical coordinates are （0,0） Coordinate points intersect . In abscissa [-1,-0.5,1] They correspond to each other [bad, normal, good]

• Result chart ：
  

Two ：
x = [‘1 month ’, ‘2 month ’, ‘3 month ’, ‘4 month ’, ‘5 month ’, ‘6 month ’, ‘7 month ’, ‘8 month ’, ‘9 month ’, ‘10 month ’, ‘11 month ’, ‘12 month ’]
y = [123, 145, 152, 182, 147, 138, 189, 201, 203, 211, 201, 182]
requirement ：
1. The line type and marked points are shown in the figure , You can refer to this chapter to find the corresponding code
2. The color is set to rgba(255, 0, 0, 0.5)
3. The line width is 2, The marking size is 15

• Result chart ：